Predicting the Finite Population Distribution Function under a Multilevel Model

Abstract

Chambers and Dunstan proposed a model-based predictor of the population distribution function that makes use of auxiliary population information under a general sampling design. Subsequently, Rao, Kovar, and Mantel proposed design-based ratio and difference predictors of the population distribution function that also use this auxiliary information. Both predictors (CD and RKM) assume a single level model for the target population. In this article we develop predictors of the finite population distribution function for a population that follows a multilevel model. These new predictors use the same smearing approach underpinning the CD predictor. We compare our new predictors with the CD and RKM predictors via design-based simulation, and show that they perform better than these single level predictors when there is significant intra-cluster correlation. The performances of these new two level predictors are also examined via an empirical study based on data from a large-scale UK business survey aimed at estimating the distribution of hourly pay rates. AMS subject classification: Primary 62G30, Secondary 62G32